| Abstrakt: |
Ontology merging is an important task in ontology engineering to date. However, despite the efforts devoted to ontology merging, the incorporation of relevant features of ontologies such as axioms, individuals and annotations in the output ontologies remains challenging. Consequently, existing ontology-merging solutions produce new ontologies that do not include all the relevant semantic features from the candidate ontologies. To address these limitations, this paper proposes a novel algorithm for multi-criteria ontology merging that automatically builds a new ontology from candidate ontologies by iteratively updating an RDF graph in the memory. The proposed algorithm leverages state-of-the-art Natural Language Processing tools as well as a Machine Learning-based framework to assess the similarities and merge various criteria into the resulting output ontology. The key contribution of the proposed algorithm lies in its ability to merge relevant features from the candidate ontologies to build a more accurate, integrated and cohesive output ontology. The proposed algorithm is tested with five ontologies of different computing domains and evaluated in terms of its asymptotic behavior, quality and computational performance. The experimental results indicate that the proposed algorithm produces output ontologies that meet the integrity, accuracy and cohesion quality criteria better than related studies. This performance demonstrates the effectiveness and superior capabilities of the proposed algorithm. Furthermore, the proposed algorithm enables iterative in-memory update and building of the RDF graph of the resulting output ontology, which enhances the processing speed and improves the computational efficiency, making it an ideal solution for big data applications. [ABSTRACT FROM AUTHOR] |
